U.S. patent number 11,087,466 [Application Number 16/622,375] was granted by the patent office on 2021-08-10 for methods and system for compound ultrasound image generation.
This patent grant is currently assigned to KONINKLIJKE PHILIPS N.V.. The grantee listed for this patent is KONINKLIJKE PHILIPS N.V.. Invention is credited to Ji Cao, William Hou, Emil George Radulescu, Jean-Luc Francois-Marie Robert, Francois Guy Gerard Marie Vignon.
United States Patent |
11,087,466 |
Vignon , et al. |
August 10, 2021 |
Methods and system for compound ultrasound image generation
Abstract
The invention provides a method for generating a compound
ultrasound image. The method includes acquiring and beamforming
channel data. Using the beamformed channel data a plurality of
images, each image comprising a plurality of pixels, of a region of
interest are obtained and an image information metric, wherein the
image metric is associated with a pixel of the plurality of pixels,
is assessed. The acquiring of the plurality of images and the
assessment of the image metric are performed in parallel. For each
image of the plurality of images: a per-pixel weighting for each
pixel of the plurality of pixels based on the assessment of the
image information metric is determined and applied to each pixel of
the plurality of pixels. Finally a compound ultrasound image is
generated based on the plurality of weighted pixels of the
plurality of images.
Inventors: |
Vignon; Francois Guy Gerard
Marie (Andover, MA), Hou; William (Briarcliff Manor,
NY), Robert; Jean-Luc Francois-Marie (Cambridge, MA),
Radulescu; Emil George (Ossining, NY), Cao; Ji (Bothell,
WA) |
Applicant: |
Name |
City |
State |
Country |
Type |
KONINKLIJKE PHILIPS N.V. |
Eindhoven |
N/A |
NL |
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|
Assignee: |
KONINKLIJKE PHILIPS N.V.
(Eindhoven, NL)
|
Family
ID: |
62748946 |
Appl.
No.: |
16/622,375 |
Filed: |
June 18, 2018 |
PCT
Filed: |
June 18, 2018 |
PCT No.: |
PCT/EP2018/066053 |
371(c)(1),(2),(4) Date: |
December 13, 2019 |
PCT
Pub. No.: |
WO2018/234209 |
PCT
Pub. Date: |
December 27, 2018 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20200202518 A1 |
Jun 25, 2020 |
|
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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62523318 |
Jun 22, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T
5/50 (20130101); G06T 7/0012 (20130101); G06T
7/73 (20170101); A61B 8/4245 (20130101); G06T
2207/20182 (20130101); G06T 2207/10132 (20130101); G06T
2207/20221 (20130101); G06T 2207/30004 (20130101) |
Current International
Class: |
A61B
8/00 (20060101); G06T 7/00 (20170101); G06T
7/73 (20170101); G06T 5/50 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
Other References
Vincent Grau and J. Alison Noble "Adaptive Multiscale Compounding
Using Phase Information", Oct. 26-29, 2005, Medical Image Computing
and Computer-Assisted Intervention--MICCAI 2005, 8th International
conference, pp. 589-596. (Year: 2005). cited by examiner .
International Search Report and Written Opinion for International
Application Serial No. PCT/EP2018/066053, filed Jun. 18, 2018, 13
pages. cited by applicant .
Entrekin, et al., "Real Time Spatial Compound Imaging in breast
ultrasound: technology and early clinical experience", Medicamundi,
vol. 43, Issue 3, Sep. 1999, pp. 35-43. cited by applicant .
Tran, et al., "Adaptive Spatial compounding for improving
ultrasound images of the epidural space", Medical Imaging 2007,
Ultrasonic Imaging and Signal Processing, Proc. of SPIE, vol. 6513,
pp. 65130W-1 to 65130W-12. cited by applicant .
Cheung, et al., "Enhancement of Needle Visibility in
Ultrasound-Guided Percutaneous Procedures", Ultrasound in Med.
& Biol., vol. 30, No. 5, pp. 617-624. cited by applicant .
Zhuang, et al., "Adaptive Spatial Compounding for Needle
Visualization", 2011 IEEE International, Ultrasonics Symposium, 4
pages. cited by applicant .
Rajpoot, et al., "Multiview Fusion 3-D Echocardiography: Improving
the Information and Quality of Real-Time 3-D Echocardiography",
Ultrasound in Med. & Biol. vol. 37, No. 7, pp. 1056-1072. cited
by applicant.
|
Primary Examiner: Harandi; Siamak
Parent Case Text
RELATED APPLICATIONS
This application is the U.S. National Phase application under 35
U.S.C. .sctn. 371 of International Application No.
PCT/EP2018/066053, filed on Jun. 18, 2018, which claims the benefit
of and priority to U.S. Provisional No. 62/523,318, filed Jun. 15,
2017. These applications are hereby incorporated in their entirety
by reference herein.
Claims
The invention claimed is:
1. A method for generating a compound ultrasound image, the method
comprising: acquiring channel data; beamforming the channel data;
in parallel: obtaining a plurality of images from the beamformed
channel data, each image comprising a plurality of pixels, of a
region of interest; and assessing an image information metric of
the beamformed channel data, wherein the image information metric
corresponds to each pixel of the plurality of images; and for each
image of the plurality of images: determining a per-pixel weighting
for each pixel of the plurality of pixels based on the assessment
of the image information metric; and applying the per-pixel
weighting to each pixel of the plurality of pixels; and generating
a compound ultrasound image based on the plurality of weighted
pixels of the plurality of images.
2. A method as claimed in claim 1, wherein the plurality of pixels
are volumetric pixels.
3. A method as claimed in claim 1, wherein each image of the
plurality of images comprises a viewing angle of the region of
interest, wherein the viewing angle of each image is different.
4. A method as claimed in claim 1, wherein the image information
metric comprises at least one of a feature and an orientation.
5. A method as claimed in claim 1, wherein the assessing of the
image information metric comprises assessing a coherence metric of
the beamformed channel data.
6. A method as claimed in claim 5, wherein the coherence metric
comprises at least one of: a coherence factor; a dominance of an
eigenvalue of a covariance matrix; and a Wiener factor.
7. A method as claimed in claim 1, wherein the generation of the
compound ultrasound image comprises performing at least one of:
spatial; temporal; or frequency compounding on the plurality of
weighted pixels.
8. A method as claimed in claim 1, wherein the generation of the
compound ultrasound image comprises at least one of retrospective
dynamic transmit (RDT) focusing and incoherent RDT focusing.
9. A method as claimed in claim 1, wherein the generation of the
compound ultrasound image is performed in a multi-scale
fashion.
10. A method as claimed in claim 1, wherein the method further
comprises assigning brightness values to the plurality of pixels of
each image based on the assessment of the image information
metric.
11. A method as claimed in claim 10, wherein the determining of the
weighting for each pixel is based on at least one of a maximum
brightness value; a mean brightness value; and a minimum brightness
value of the plurality of pixels across the plurality of
images.
12. A method as claimed in claim 11, wherein the method further
comprises: generating a mean brightness value image based on the
mean brightness value of the plurality of pixels across the
plurality of images; subtracting the mean brightness value image
from the compound ultrasound image, thereby generating a difference
image; applying a low pass filter to the difference image; and
summing the mean brightness value image and the difference image,
thereby generating a speckle filtered compound ultrasound
image.
13. A computer program stored on a non-transitory medium, the
computer program comprising computer program code means which is
adapted, when said computer program is run on a computer, to
implement the method of claim 1.
14. A system for generating a compound ultrasound image, the system
comprising: ultrasound probe, adapted to acquire channel data;
beamformer, adapted to apply beamforming to the channel data; a
controller adapted to: in parallel: obtain a plurality of images
from the beamformed channel data, each image comprising a plurality
of pixels, of a region of interest; and assess an image information
metric of the beamformed channel data, wherein the image
information metric corresponds to each pixel of the plurality of
images; and for each image of the plurality of images: determine a
per-pixel weighting for each pixel of the plurality of pixels based
on the assessment of the image information metric; and apply the
per-pixel weighting to each pixel of the plurality of pixels; and a
pixel compounder, adapted to generate a compound ultrasound image
based on the plurality of weighted pixels of the plurality of
images.
15. A system as claimed in claim 14, wherein the ultrasound probe
comprises an electronic steering unit adapted to alter a viewing
angle of the ultrasound probe.
16. A system as claimed in claim 14, wherein, to obtain the
plurality of images, the controller is adapted to: perform at least
one of envelope detection, log compression, scan conversion, or
spatial registration using the beamformed channel data in parallel
with the assessment of the image information metric.
17. A method as claimed in claim 1, wherein, the obtaining the
plurality of images comprises: performing at least one of envelope
detection, log compression, scan conversion, or spatial
registration using the beamformed channel data in parallel with the
assessment of the image information metric.
18. A system as claimed in claim 14, wherein: the controller
comprises a first processing pathway and a different, second
processing pathway in parallel with the first processing pathway,
and to obtain the plurality of images and assess the image
information metric in parallel, the controller is adapted to:
obtain the plurality of images using the first processing pathway;
and assess the image information metric using the second processing
pathway.
Description
RELATED APPLICATION
This application claims the benefit of and priority to U.S.
Provisional Application No. 62/523,318, filed Jun. 22, 2017, which
is incorporated by reference in its entirety.
FIELD OF THE INVENTION
This invention relates to ultrasound imaging and, more
particularly, to generating a compound ultrasound image.
BACKGROUND OF THE INVENTION
In general, generating a compound image in an ultrasound system
consists of imaging the same medium with different insonation
parameters and averaging the resulting views.
For example, in the case of spatial compounding the medium is
imaged at various viewing angles, each generating a different view.
The views are then averaged to generate the compound ultrasound
image. This results in decreased speckle variance and increased
visibility of plate-like scatterers (boundaries) along with other
image quality improvements. The averaging reduces speckle noise and
improves image quality because they depict similar anatomical
features, despite the views having different noise patterns. In
addition, certain structures that are only visible, or more
visible, at certain imaging angles, may be enhanced through spatial
compounding.
The speed of sound varies by as much as 14% in soft tissue, meaning
that a slight positioning mismatch of structures may be present in
the different views. In this case, the compounding may lead to
blurring. In addition, the compounding may lead to: the sidelobes
of the point-spread functions at different view angles being
averaged, resulting in increased smearing of tissue in cysts;
grating lobes from the different angled views corrupting the
compound ultrasound image; and structures that are only visible at
a given angle not being sufficiently enhanced because the optimal
view is averaged with other, sub-optimal views. These combined
effects result in a decreased contrast of the compounded ultrasound
image compared to the single-view images.
SUMMARY OF THE INVENTION
The present invention provides systems and methods for generating a
compound ultrasound image whilst maintaining image contrast without
requiring significant additional hardware.
According to examples in accordance with an aspect of the
invention, there is provided a method for generating a compound
ultrasound image, the method comprising:
acquiring channel data;
beamforming the channel data;
in parallel, using the beamformed channel data to: obtain a
plurality of images, each image comprising a plurality of pixels,
of a region of interest; and assess an image information metric for
each pixel of a plurality of images; and
for each image of the plurality of images: determining a per-pixel
weighting for each pixel of the plurality of pixels based on the
assessment of the image information metric; and applying the
per-pixel weighting to each pixel of the plurality of pixels;
and
generating a compound ultrasound image based on the plurality of
weighted pixels of the plurality of images.
This method generates a compound ultrasound image from a plurality
of weighted ultrasound images. In this way, it is possible to
generate a compound ultrasound image where the key features are
preferentially weighted based on a predetermined image metric. By
performing the image acquisition and image metric assessment in
parallel, it is possible to significantly increase the efficiency
of the method and reduce the time required to generate the compound
ultrasound image. In addition, as the beamformed channel data
typically contains more detail than the conventional B-mode
ultrasound image, the image metric assessment based on the
beamformed channel data may be more accurate than an assessment
based on the image itself, thereby increasing the accuracy of the
weightings, and so the compound ultrasound image.
The pixels of the compound ultrasound image may be thought of as a
weighted average of the pixels of the plurality of images obtained
from the beamformed channel data.
In an embodiment, the plurality of pixels are volumetric
pixels.
In this way, it is possible to generate a three dimensional
compound ultrasound image.
In an arrangement, each image of the plurality of images comprises
a viewing angle of the region of interest, wherein the viewing
angle of each image is different.
In this way, it is possible for the images to provide uncorrelated
content from each view, meaning that anisotropic features appearing
under only a few viewing angles are more likely to be captured in
one of the plurality of images. By capturing these features in at
least one of the plurality of images, the feature may be weighted
to appear more clearly in the compound ultrasound image, thereby
increasing the accuracy of the final image.
In some arrangements, the image metric comprises at least one of a
feature and an orientation.
In this way, it is possible to either identify a common feature or
identify the changes in orientation between each of the images,
which may then be used in the generation of the compound ultrasound
image.
In some embodiments, the assessing of the image metric comprises
assessing a coherence metric of the beamformed channel data.
In this way, it is possible to distinguish between low coherence
signals, such as system noise, high coherence signals, such as
signals from a point scatterer, and intermediate coherence signals,
such as speckle. In this way, the coherence metric may be used to
apply appropriate weightings to minimize noise and highlight
important features in the region of interest.
In an embodiment, the coherence metric comprises at least one of: a
coherence factor; a dominance of an eigenvalue of a covariance
matrix; and a Wiener factor.
In some embodiments, the generation of the compound ultrasound
image comprises performing at least one of: spatial; temporal; or
frequency compounding on the weighted pixels.
In this way, it is possible to compound images obtained from:
different viewing angles; independent acoustic windows; and
different imaging frequencies, respectively.
In an arrangement, the generation of the compound ultrasound image
comprises at least one of retrospective dynamic transmit (RDT)
focusing and incoherent RDT focusing.
In some arrangements, the generation of the compound ultrasound
image is performed in a multi-scale fashion.
In this way, it is possible to separate the image data based on the
spatial frequencies of the image. By separating the image data by
spatial frequencies, low spatial frequency signals, which may
contain structures such as cysts, may be used in the image metric
assessment, whilst high spatial frequency signals, which may
contain speckle, may be discarded.
In an embodiment, the method further comprises assigning brightness
values to the plurality of pixels of each image based on the
assessment of the image metric.
In this way, it is possible to generate a visual representation of
the beamformed channel data assessment. In addition, in the case
where more than one image metric is used, the brightness value may
provide a simple representation of multiple complex parameters.
In a further embodiment, the determining of the weighting for each
pixel is based on at least one of a maximum brightness value; a
mean brightness value; and a minimum brightness value of the
plurality of pixels across the plurality of images.
In this way, the maximum brightness value pixels may be weighted
highly for important features of the region of interest, minimum
brightness value pixels may be weighted highly in areas of high
system noise, thereby removing clutter from the compound ultrasound
image, and mean brightness value pixels may be weighted highly in
areas of speckle signals.
In a yet further embodiment, the method further comprises:
generating a mean brightness value image based on the mean
brightness value of the plurality of pixels across the plurality of
images;
subtracting the mean brightness value image from the compound
ultrasound image, thereby generating a difference image;
applying a low pass filter to the difference image; and
summing the mean brightness value image and the subtraction image,
thereby generating a speckle filtered compound ultrasound
image.
In this way, it is possible eliminate speckle artifacts from the
compound ultrasound image.
According to examples in accordance with an aspect of the
invention, there is provided a computer program comprising computer
program code means which is adapted, when said computer program is
run on a computer, to implement the method described above.
According to examples in accordance with an aspect of the
invention, there is provided a system for generating a compound
ultrasound image, the system comprising:
an ultrasonic probe, adapted to acquire channel data;
a beamforming module, adapted to apply beamforming to the channel
data;
a controller adapted to: in parallel, use the beamformed channel
data to: obtain a plurality of images, each image comprising a
plurality of pixels, of a region of interest; and assess an image
information metric for each pixel of a plurality of images; and for
each image of the plurality of images: determine a per-pixel
weighting for each pixel of the plurality of pixels based on the
assessment of the image information metric; and apply the per-pixel
weighting to each pixel of the plurality of pixels; and
a pixel compounder, adapted to generate a compound ultrasound image
based on the plurality of weighted pixels of the plurality of
images.
In an embodiment, the ultrasonic probe comprises an electronic
steering unit adapted to alter the viewing angle of the ultrasonic
probe.
BRIEF DESCRIPTION OF THE DRAWINGS
Examples of the invention will now be described in detail with
reference to the accompanying drawings, in which:
FIG. 1 shows a schematic diagram of a compound ultrasound imaging
system;
FIG. 2 shows a set of mathematical definitions and
relationships;
FIG. 3 shows a method of the invention;
FIG. 4 shows a comparison between pixel brightness maps and a
coherence factor map of an ultrasound image; and
FIG. 5 shows the adaptive brightness maps of FIG. 4 after the
application of a speckle reduction method.
DETAILED DESCRIPTION OF THE EMBODIMENTS
The invention provides a method for generating a compound
ultrasound image. The method includes acquiring and beamforming
channel data. Using the beamformed channel data a plurality of
images, each image comprising a plurality of pixels, of a region of
interest are obtained and an image information metric, wherein the
image metric is associated with a pixel of the plurality of pixels,
is assessed. The acquiring of the plurality of images and the
assessment of the image metric are performed in parallel. For each
image of the plurality of images: a per-pixel weighting for each
pixel of the plurality of pixels based on the assessment of the
image information metric is determined and applied to each pixel of
the plurality of pixels. Finally a compound ultrasound image is
generated based on the plurality of weighted pixels of the
plurality of images.
FIG. 1 is a schematic representation of an ultrasound system 100
adapted to generate compound ultrasound images. The system includes
an ultrasound probe 114 connected by a cable 116 to an image
formation module 102. The image formation module contains a channel
beamformer 166 and a beamspace beamformer X. The apparatus further
comprises an image content assessment module 154, a weight
determination module 156, an image processor 110, and an imaging
display 112.
The imaging probe 114 is used to perform several observations of
the medium of interest 138 under different conditions, such as
varying transmit/receive angle or transmit/receive frequency. For
example, three transmit angles may be used to produce three
respective images 126, 128, 130. The images may vary in transmit or
receive angle or both, in the case of spatial compounding, or in
transmit or receive frequency or both in the case of frequency
compounding, or in both angle and frequency.
A scan conversion and spatial registration module XX ensures that
all images are spatially registered, meaning that each pixel 137 of
an image spatially corresponds to a pixel of each of the remaining
images, and spatially corresponds to a pixel of the final compound
ultrasound image 190 to be formed. The images may be
two-dimensional or three-dimensional.
In this case, the different images 126, 128, 130 of a region of
interest 138 are obtained from a single, acoustic window 140 on an
outer surface 142, or skin, of an imaging subject 144, such as a
human patient or animal. Alternatively, or in addition, more than
one acoustic window on the outer surface 142 may be utilized for
acquiring views having different angles. The probe 114 may be moved
from window to window, or additional probes may be placed at each
acoustic window. In the case of multiple acoustic windows, temporal
compounding may be performed on the multiple images.
The image formation module 102 comprises a beamforming module 152.
The beamforming module 152 contains an electronic steering module,
also referred to as a channel beamformer 166, and a beamforming
summation module 168. The electronic steering module 166 includes a
beamforming delay module 170 adapted to introduce a delay to
various channel data signals. The beamforming module 152 may also
comprise a beamspace beamforming module X. The image formation
module 102 further comprises an envelope detection module XXX and a
logarithmic compression module 162.
The image content assessment module 154 may include a classifier
module 172, a coherence factor module 174, a covariance matrix
analysis module 176, and a Wiener factor module 178. In some cases,
the image content assessment module may further include other
modules to measure local coherence of signals by way of alternative
coherence metrics.
The image processor 110 includes a pixel compounder 160. The pixel
compounder 160 includes a spatial compounder 180, a temporal
compounder 181, and a frequency compounder 182. Inputs to the pixel
compounder 160 include pixels 137a, 137b, 137c, of the three images
126, 128, 130, that spatially correspond to the current pixel of
the compound image 191 to be formed, i.e., the current compound
image pixel. These inputs are accompanied by weighting inputs 180a,
180b and 180c from respective weighting images 184, 186, 188
determined by the weight determination module 156. The output of
the pixel compounder 160 is a compound pixel 191 of the compound
ultrasound image 190 being formed.
The operational principles of the coherence factor module 174 and
covariance matrix analysis module 176 are described below.
With regard to coherence estimation, as performed by the coherence
factor module 174, let S(m, n, tx, rx) denote complex RF,
beamforming-delayed channel data 192, i.e. the beamformed channel
data formed after the application of beamforming delays, by the
channel beamformer 166, but before beamsumming, by the beamforming
summation module 168. Here, m is the imaging depth/time counter or
index, n the channel index, tx the transmit beam index, and rx the
receive beam index. A coherence factor (CF) or "focusing criterion"
at a pixel (m, rx), or field point, 137 with a single receive beam
rx is calculated as follows:
.function..ident..times..times..function..times..times..function.
##EQU00001##
where N is the number of channels.
In the case that multiple transmit beams are incorporated into the
CF estimation; the CF formula may be redefined as:
.function..ident..DELTA..DELTA..times..times..function..DELTA..DELTA..tim-
es..times..times..function..times..times. ##EQU00002##
where .DELTA. is a tunable parameter used to perform averaging over
multiple transmit events in the case of coherent transmit
compounding being used in the beamformer.
This definition, like the ones that follow, is repeated in FIG. 2.
The assessing of an image metric with respect to pixel (m, rx) by
computing CF(m, rx) is performed in parallel to the acquiring of
the images 126, 128 and 130. In addition, the assessment of the
delayed channel data 192 commences no later than the beamforming
summation, i.e., the summation .SIGMA..sub.n=1.sup.NS(m, n, tx,
rx).
As mentioned above, the pixel (m, rx) 137 is a function of
position. The coherence estimation operates on the delayed channel
data 192. The CF(m, rx) estimate, or result of the estimation, 204
may include summing, over multiple transmit beams, a
squared-magnitude function 206 and a squared beamsum 208, which is
the summed result of beamforming. The function 206 and beamsum 208
are both formed by summing over the channels of the channel
data.
Referring now to the covariance matrix analysis, performed by the
covariance matrix analysis module, let R(m, rx) denote a covariance
matrix, or correlation matrix, 210 at the pixel (m, rx) obtained by
temporal averaging over a range 214 of time or spatial depth:
.function..ident..times..times..times..times..function..times..function..-
times..times. ##EQU00003##
where:
.function..function..function..function..times..times.
##EQU00004##
and s.sup.H is the Hermitian transpose of s. p indicates the depth
sample index and d is a tunable parameter used to define a depth
window over which the covariance is estimated. This may be of the
order of the transmit pulse length, which is typically several
wavelengths.
As R(m, rx) is a positive semidefinite matrix, all of its
eigenvalues 212 are real and positive. Denoting the eigenvalues as
{y.sub.i (m, rx)}N.sub.i=1.sup.N, with y.sub.i.gtoreq.y.sub.i+1,
the trace of R(m, rx) may be written as:
Tr{R(m,rx)}.ident..SIGMA..sub.i=1.sup.NR.sub.ii(m,rx)=.SIGMA..sub.i=1.sup-
.Ny.sub.i(m,rx). (definition 4)
The dominance 216 of the first eigenvalue 218 is represented
as:
.function..ident..gamma..function..times..function..times..times.
##EQU00005##
The dominance is infinite if y.sub.i(m, rx)=0 for i.gtoreq.2, i.e.
if the rank of R(m, rx) is 1, as Tr{R(m, rx)}=y.sub.1(m, rx), and
finite otherwise. Summing over several transmits, also referred to
as beam averaging, may also be applied in covariance matrix
analysis as follows:
.function..ident..times..times..times..times..times..times..DELTA..DELTA.-
.times..function..times..function..times..times. ##EQU00006##
where:
.function..function..function..function..times..times.
##EQU00007##
Another way of combining transmits is to form the covariance matrix
from data generated by an algorithm that recreates focused transmit
beams retrospectively. An example utilizing RDT focusing is as
follows, and, for other algorithms such as incoherent RDT, plane
wave imaging and synthetic aperture beamforming, analogous
eigenvalue dominance computations apply:
.function..ident..times..times..times..times..function..times..function..-
times. ##EQU00008## .function..function..function..function.
##EQU00008.2##
wherein, S.sub.RDT (p, n, rx) are the dynamically
transmit-beamformed, complex RF channel data obtained by performing
retrospective dynamic transmit (RDT) focusing on the original
channel data S(m, n, tx, rx). As with the coherence factor
assessment, the assessing of an image metric with respect to (m,
rx) by computing R(m, rx) is performed in parallel to the acquiring
of the images 126, 128 and 130. In addition, the assessment of the
delayed channel data 192 commences no later than the beamforming
summation.
In the above approach, CF.sub.0(m, rx) or CF(m, rx) may, as with
the eigenvalue dominance, also be obtained by way of temporal
averaging over a range 214 of time or spatial depth 140.
According to J. R. Robert and M. Fink, "Green's function estimation
in speckle using the decomposition of the time reversal operator:
Application to aberration correction in medical imaging," J.
Acoust. Soc. Am., vol. 123, no. 2, pp. 866-877, 2008, the dominance
of the first eigenvalue ev.sub.d (m, rx) may be approximated by
1/(1-CF.sub.1(m, rx)), where CF.sub.1(m, rx) is a coherence factor
obtained from channel data S(m, n, tx, rx). Temporal averaging,
averaging over multiple transmit beams and/or RDT may be applied in
calculating CF.sub.1(m, rx). Inversely, the coherence factor may be
approximated based on the eigenvalue dominance derived with
appropriate averaging.
In addition to the CF metric and eigenvalue dominance metric,
another example of an image metric that may be used is the Wiener
factor, which is applicable in the case of RDT and IRDT. The Wiener
factor module 178 for deriving the Wiener factor operates on the
following principles.
K ultrasound wavefronts (transmits) sequentially insonify the
medium 138. The waves backscattered by the medium are recorded by
the transducer array of the ultrasonic probe and beamformed in
receive to focus on the same pixel 137. It is assumed here that the
pixel is formed by RDT, or IRDT, focusing.
The result is a set of K receive vectors denoted as r.sub.i(P),
where i=1, . . . , K, of size N samples (one sample per array
element) that correspond to a signal contributing to pixel P 137.
Each of the vectors can be seen as a different observation of the
pixel 137. The entries of r.sub.i(P) are complex having both a
non-zero real component and imaginary component.
Each of the receive vectors is weighted by the apodization vector
a, which is for example a Box, Hanning, or Riesz window, and summed
across the receive elements. This yields K beam-sum values that
correspond to the Sample Values (SV) as obtained with the K
different insonifications:
{SV.sub.1(P)=a.sup.Hr.sub.1(P);SV.sub.2(P)=a.sup.Hr.sub.2(P); . . .
;SV.sub.K(P)=a.sup.Hr.sub.K(P)} (expression 1)
The collection of these K sample values is called the "RDT vector."
Note that the RDT sample value is obtained by summing the values of
the RDT vector as follows:
SV.sub.RDT=.SIGMA..sub.i=1.sup.Ka.sup.Hr.sub.K(P) (expression
2)
The Wiener factor is given as:
.function..times..times..function..times..times..function..times..times.
##EQU00009##
The numerator of expression 3 is the square of the coherent sum of
the elements of the RDT vector, i.e. the RDT sample value squared.
The denominator is the incoherent sum of the squared elements of
the RDT vector. In other words, if the incoherent RDT sample value
(SV.sub.IRDT) is defined as the square root of the numerator,
then:
.function..function..function. ##EQU00010##
The Wiener factor is the ratio between the coherent RDT energy and
the incoherent RDT energy. Thus, it may be considered as a
coherence factor in beam space. It may be used as an image metric
for RDT and IRDT focusing. Once again, the assessing of local image
content with respect to pixel 137 by computing w.sub.wiener(P) is
performed in parallel to the acquiring of the images 126, 128 and
130. In addition, the assessment of the delayed channel data 192
commences no later than the beamforming summation i.e. the
summation .SIGMA..sub.i=1.sup.Ka.sup.Hr.sub.K(P).
Direct image metrics may also be used in lieu of the signal-based
image metrics, such as coherence factor. For example, known
confidence metrics in the literature are usually based on the local
gradient and Laplacian of the image. See, for example, Frangi et
al, "Multiscale vessel enhancement filtering", MICCAI 1998. A
"confidence factor" may be computed from the pre-compressed data as
follows: at each pixel, a rectangular box of approximately 20 by 1
pixels is rotated with the spatially corresponding pixel 180a-180c
in the middle of the box. The box is rotated from 0 to 170 degrees
by increments of 10 degrees. For each orientation of the box, the
mean pixel values inside the box are recorded. The final metric is
equal to the maximum of this metric across all angles.
FIG. 3 shows a method 300 of the invention.
In step 302, channel data is acquired by way of an ultrasonic
probe. The channel data may comprise data relating to several
observations of a region of interest.
In step 304, the channel data is beamformed. The beamforming may be
performed by the image acquisition module 102, and more
specifically by the beamforming delay module 170. The beamforming
delay module may apply channel-specific delays to the channel data,
thereby yielding the beamformed channel data 192.
In step 306, the beamformed channel data is used by the image
acquisition module 102 to obtain a plurality of images 126-130. The
plurality of images may each comprise a different viewing angle of
a region of interest.
In parallel to step 306, in step 308, the beamformed channel data
is used by the image content assessment module 154 to assess an
image information metric. The image information metric may be a
coherence metric, such as: a coherence factor; a covariance matrix;
and, in most particularly in the case of RDT/IRDT focusing, a
Wiener factor (although a Wiener factor may be used without
RDT/IRDT focusing). These factors may be assessed as discussed
above. Additionally, any combination of image metrics may be used
to assess the beamformed channel data, such as coherence factor and
Wiener factor or coherence factor and covariance matrix eigenvalue
dominance. It should be noted that coherence factor and covariance
matrix image metrics may be used in any scenario, regardless of
whether RDT or IRDT focusing are employed. Alternatively, any other
measure relating to the coherence of the signals of the channel may
be assessed as the image information metric. The image information
metrics are determined for image locations which spatially
correspond to the images obtained in step 306.
In step 310, the assessed image information metric is used to
determine a per-pixel weighting for each spatially corresponding
pixel of an image.
In step 312, the per-pixel weightings are applied to each pixel of
the image.
Steps 310 and 312 are repeated for each of the plurality of
images.
In step 314, the compound ultrasound image 190 is generated based
on the plurality of weighted pixels of the images. Image to image
motion compensation, or plane compounding, may be applied to reduce
motion artifacts in the final compound ultrasound image.
The final compound ultrasound image, I.sub.compound, may be
represented as:
I.sub.compound=.SIGMA..sub.i=1.sup.Nw.sub.iI.sub.i
where: w.sub.i is the weight to be applied locally to the image,
I.sub.i. The images are compounded on a per-pixel basis, meaning
that if the pixels of the images and weight maps are indexed x and
y, then the equation becomes:
.times..times..times. ##EQU00011##
As described above, the w.sub.i.sub.x,y are derived from measure of
coherence of the channel data, but may also be image-based.
In some cases, classification may be performed on the image
information metric to determine whether the image information
metric comprises a feature or an orientation of the region of
interest. This classification may be performed over any spatial
range of the image, for example, over a 124 pixel cube centered on
the current pixel being assessed. If either a feature, such as a
blood vessel, or an orientation are determined to be present in the
image information metric, the pixel being assessed may be
classified as important. This classification may then be taken into
account during the determination of the per-pixel weightings.
For example, a weight of unity may be assigned to a pixel of one of
the plurality of images that was as marked important and a weight
of zero assigned to the remaining pixels of the remaining images.
Alternatively, the weight determination may differentiate between
found features and found orientations, giving, for example,
priority to features. Another alternative is to split the weighted
average between two pixels that were both marked as important.
Also, classifying of importance may, instead of garnering the full
weight of unity, be accorded a high weight such as 0.75, based on
the image information metric analysis, affecting the weighting for
the other spatially corresponding pixels.
Brightness maps may be generated form the plurality of images. For
example, a maximum brightness value map may be constructed by
selecting, for each pixel of the maximum brightness value map, the
spatially corresponding pixel having the highest brightness value
across the plurality of obtained images. Similarly, mean and
minimum brightness maps may also be generated.
The maximum brightness value map constitutes an image that enhances
the visibility of anisotropic structures; however, tissue smearing
is maximized and contrast is deteriorated in this brightness map.
In the mean brightness value map, the benefits of smoothing out
speckle areas are realized. The minimum brightness value map
depicts anisotropic structures poorly, but advantageously yields
low brightness values inside cysts. It is beneficial to not enhance
cyst areas, and not to bring sidelobe clutter into cysts.
Additionally, a normalized image information metric map may also be
generated.
The weightings may be determined as a function of the brightness
maps, resulting in the following equation:
I.sub.compound=w.sub.minI.sub.min+w.sub.meanI.sub.mean+w.sub.maxI.sub.max-
,
where I.sub.min, I.sub.mean, and I.sub.max are respectively the
minimum, mean, and maximum brightness value maps over the images,
respectively. As before, this may also be expressed in a pixel-wise
form.
Exemplary implementations, based on the coherence factor (CF), are
discussed below. More generally, based on the image information
metric map, it is possible to determine a weight to assign to the
minimum, mean and maximum spatially corresponding pixels to form a
final compound ultrasound image, which contains all structures with
maximum visibility and all cysts with maximum contrast.
Two possible implementations are described below, the first of
which does not use the minimum brightness image and the second of
which does. Using the minimum image increases image contrast by
decreasing cyst clutter but may also result in unwanted signal
reduction from real structures.
In a first implementation, a weighted average of the pixels is
taken from the mean and maximum images. The three rules of this
implementation are: 1) when the CF is above a given threshold
t.sub.max, select the pixel from the maximum image; 2) when the CF
is below a given threshold t.sub.min, select the pixel from the
mean image; and 3) when the CF lies between the two threshold
values, combine the two pixels. This can be formalized
mathematically as follows:
Normalize CF between t.sub.min and t.sub.max:
.function..function. ##EQU00012## Determine the weights based on
the normalized CF: w.sub.mean=1-CF.sub.norm;
w.sub.max=CF.sub.norm
Accordingly, instead of compounding the obtained images 126-130
directly, each compound pixel 191 is the weighted average of its
counterpart in the mean brightness map and its counterpart in the
maximum brightness map, those two counterpart pixels being weighted
respectively by w.sub.mean and w.sub.max. The weights may also have
a quadratic, polynomial, or exponential expression.
The second implementation finds the weighted average of the
minimum, mean and maximum images. In this case, the three rules
are: 1) when the CF is above a given threshold t.sub.max, select
the pixel from the maximum image; 2) when the CF is below a given
threshold t.sub.min, select the pixel from the minimum image; and
3) in between, combine the pixels from the minimum, mean and
maximum images, although some potential values of the CF will
exclusively select the pixel from the mean image.
This can be formalized mathematically as follows:
Normalize CF between t.sub.min and t.sub.max:
.function..function. ##EQU00013## Determine the weights based on
the normalized CF:
w.sub.min=(1-CF.sub.norm).sup.2;w.sub.max=(CF.sub.norm).sup.2;w.sub.mean=-
1-w.sub.min-w.sub.max
The weights may also have a linear, polynomial, or exponential
expression.
Speckle artifacts introduced by the adaptive method can be removed,
while retaining the contrast gains, as follows. The mean brightness
value image is subtracted from the compound ultrasound image
created in step S314. The resulting difference image is low-pass
filtered and the low-pass-filtered image is added to the mean image
to yield a despeckled image. The low-frequency image changes, such
as larger structures and cysts, are consequently retained, while
the higher frequency changes, such as speckle increase, are
eliminated. The low-pass filter is realizable by convolution with,
for example, a Gaussian or box kernel. A compound ultrasound image
is now ready for displaying to a user.
Alternatively, with regard to speckle reduction, a programmable
digital filter may be introduced to receive the beamformed data and
separate the data of higher spatial frequencies, which contain the
speckle signal, from the data of lower spatial frequencies. In this
multi-scale approach, a multi-scale module passes on only the lower
frequency data to the image content assessment module 154 for
adaptive compounding. The higher frequency data are assigned equal
compounding weights in the weight determination module 156.
Furthermore, different metrics and different formulas for combining
compounded sub-views into an compound image based on the metrics,
may be advantageously applied at each subscale. For instance, low
spatial frequencies may be more aggressively enhanced than higher
spatial frequencies.
Optionally, the weights determined in a neighborhood of a spatially
corresponding pixel 180a-180c may be combined, such as by
averaging. A neighborhood could be a cluster of pixels, centered on
the current pixel. In that case, compounding is performed with less
granularity, i.e., neighborhood by neighborhood, instead of pixel
by pixel. This may be employed in systems where processing power is
a limiting factor. This also has the benefit of reducing the
speckle variance of the weighted sub-images.
FIG. 4 shows a comparison between the mean and maximum brightness
maps, the coherence factor map and the first and second weighting
methods as described above.
Referring to FIG. 4, the first image 400 shows the mean brightness
map, the second image 410 shows the max brightness map, the third
image 420 shows the CF map, the fourth image 430 shows the mean-max
adaptive image described in the first implementation above and the
fifth image 440 shows the min-mean-max adaptive image described in
the second implementation above.
The adaptive images present more contrast and sharpen the aspect of
structures when compared to the mean and max brightness images. In
addition, smearing of the fascia tissue into the surrounding muscle
parenchyma is greatly reduced, especially when the minimum
brightness image is used as well, as shown by the fifth image 440.
Structures that are visible in the maximum brightness image but not
the mean brightness image are still visible in the adaptive images,
but with greater contrast than in the max image. The adaptive
images tend to have more speckle than the mean image; however, this
effect may be greatly reduced by spatial averaging/adaptive
filtering of the coherence factor map as show in FIG. 5.
Referring to FIG. 5, the first image shows the mean-max adaptive
and the min-mean-max adaptive images after the application of the
speckle reduction method described above as image 450 and 460,
respectively. It is clear by comparison to the fourth 430 and fifth
440 images of FIG. 4 that the speckle, particularly in the darker
regions, has been significantly reduced, thereby improving the
contrast of the overall image.
While the invention has been illustrated and described in detail in
the drawings and foregoing description, such illustration and
description are to be considered illustrative or exemplary and not
restrictive; the invention is not limited to the disclosed
embodiments.
For example, within the intended scope of what is proposed herein
is a computer readable medium, as described below, such as an
integrated circuit that embodies a computer program having
instructions executable for performing the process represented in
FIG. 3. The processing is implementable by any combination of
software, hardware and firmware.
A computer program can be stored momentarily, temporarily or for a
longer period of time on a suitable computer-readable medium, such
as an optical storage medium or a solid-state medium. Such a medium
is non-transitory only in the sense of not being a transitory,
propagating signal, but includes other forms of computer-readable
media such as register memory, processor cache, RAM and other
volatile memory.
Other variations to the disclosed embodiments can be understood and
effected by those skilled in the art in practicing the claimed
invention, from a study of the drawings, the disclosure, and the
appended claims. In the claims, the word "comprising" does not
exclude other elements or steps, and the indefinite article "a" or
"an" does not exclude a plurality. The mere fact that certain
measures are recited in mutually different dependent claims does
not indicate that a combination of these measures cannot be used to
advantage. Any reference signs in the claims should not be
construed as limiting the scope.
* * * * *